{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea370b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import mpl "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e8f00600",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 黑体\n",
    "plt.rcParams[\"font.family\"] = \"SimHei\"\n",
    "# plt.rcParams['font.sans-serif'] = ['SimHei']  \n",
    "# 解决无法显示符号的问题\n",
    "plt.rcParams['axes.unicode_minus'] = False    \n",
    "\n",
    "# seaborn默认主题\n",
    "# sns.set()\n",
    "# 解决Seaborn中文显示问题\n",
    "sns.set(font='SimHei',font_scale=0.8)        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af72d716",
   "metadata": {},
   "source": [
    "### matplotlib绘制散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7131756b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cbook as cbook\n",
    "\n",
    "# Load a numpy record array from yahoo csv data with fields date, open, close,\n",
    "# volume, adj_close from the mpl-data/example directory. The record array\n",
    "# stores the date as an np.datetime64 with a day unit ('D') in the date column.\n",
    "price_data = (cbook.get_sample_data('goog.npz', np_load=True)['price_data']\n",
    "              .view(np.recarray))\n",
    "price_data = price_data[-250:]  # get the most recent 250 trading days\n",
    "\n",
    "delta1 = np.diff(price_data.adj_close) / price_data.adj_close[:-1]\n",
    "\n",
    "# Marker size in units of points^2\n",
    "volume = (15 * price_data.volume[:-2] / price_data.volume[0])**2\n",
    "close = 0.003 * price_data.close[:-2] / 0.003 * price_data.open[:-2]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(delta1[:-1], delta1[1:], c=close, s=volume, alpha=0.5)\n",
    "\n",
    "ax.set_xlabel(r'$\\Delta_i$', fontsize=15)\n",
    "ax.set_ylabel(r'$\\Delta_{i+1}$', fontsize=15)\n",
    "ax.set_title('Volume and percent change')\n",
    "\n",
    "ax.grid(True)\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dedb805",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "529b1c41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c8c4488e",
   "metadata": {},
   "source": [
    "### seaborn绘制散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f2806ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载案例数据\n",
    "data_path = \"../数据加工厂/seaborn_data/tips.csv\"\n",
    "tips = pd.read_csv(data_path)\n",
    "\n",
    "# 修改为中文名\n",
    "tips.columns = [\"总账单\",\"小费\",\"性别\",\"是否吸烟\",\"星期几\",\"时间\",\"大小\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e2a6743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>总账单</th>\n",
       "      <th>小费</th>\n",
       "      <th>性别</th>\n",
       "      <th>是否吸烟</th>\n",
       "      <th>星期几</th>\n",
       "      <th>时间</th>\n",
       "      <th>大小</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     总账单    小费      性别 是否吸烟  星期几      时间  大小\n",
       "0  16.99  1.01  Female   No  Sun  Dinner   2\n",
       "1  10.34  1.66    Male   No  Sun  Dinner   3\n",
       "2  21.01  3.50    Male   No  Sun  Dinner   3\n",
       "3  23.68  3.31    Male   No  Sun  Dinner   2\n",
       "4  24.59  3.61  Female   No  Sun  Dinner   4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27482a42",
   "metadata": {},
   "source": [
    "### 不同时间段（午餐、晚餐）吸烟的人和不吸烟的人花费的账单和给的小费的关系？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5e3589a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1e97754c5e0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 765.54x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制散点图\n",
    "sns.relplot(\n",
    "    data=tips,\n",
    "    x=\"总账单\",\n",
    "    y=\"小费\",\n",
    "    col=\"时间\",\n",
    "    hue=\"是否吸烟\",\n",
    "    style=\"是否吸烟\",\n",
    "#     size=\"size\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b4b790",
   "metadata": {},
   "source": [
    "### pyecharts绘制散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a364a7de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "84e7d5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data={\"名称\":Faker.choose(),\"商家A\":Faker.values(),\"商家B\":Faker.values()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "098e6975",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>名称</th>\n",
       "      <th>商家A</th>\n",
       "      <th>商家B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>小米</td>\n",
       "      <td>59</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>三星</td>\n",
       "      <td>94</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>华为</td>\n",
       "      <td>31</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>苹果</td>\n",
       "      <td>49</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>魅族</td>\n",
       "      <td>113</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   名称  商家A  商家B\n",
       "0  小米   59   62\n",
       "1  三星   94   76\n",
       "2  华为   31   85\n",
       "3  苹果   49  128\n",
       "4  魅族  113  100"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e75bba0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"89757ce98d60406cb46df741645c9159\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_89757ce98d60406cb46df741645c9159 = echarts.init(\n",
       "                    document.getElementById('89757ce98d60406cb46df741645c9159'), 'white', {renderer: 'canvas'});\n",
       "                var option_89757ce98d60406cb46df741645c9159 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
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       "        \"#fab27b\",\n",
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       "        \"#444693\",\n",
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       "        \"#b2d235\",\n",
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       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"scatter\",\n",
       "            \"name\": \"\\u5546\\u5bb6A\",\n",
       "            \"symbolSize\": 10,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    \"\\u5c0f\\u7c73\",\n",
       "                    59\n",
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       "                [\n",
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       "                [\n",
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       "                    135\n",
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       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
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       "        },\n",
       "        {\n",
       "            \"type\": \"scatter\",\n",
       "            \"name\": \"\\u5546\\u5bb6B\",\n",
       "            \"symbolSize\": 10,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    \"\\u5c0f\\u7c73\",\n",
       "                    62\n",
       "                ],\n",
       "                [\n",
       "                    \"\\u4e09\\u661f\",\n",
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       "                [\n",
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       "                [\n",
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       "                    128\n",
       "                ],\n",
       "                [\n",
       "                    \"\\u9b45\\u65cf\",\n",
       "                    100\n",
       "                ],\n",
       "                [\n",
       "                    \"VIVO\",\n",
       "                    56\n",
       "                ],\n",
       "                [\n",
       "                    \"OPPO\",\n",
       "                    55\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"right\",\n",
       "                \"margin\": 8\n",
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       "    \"legend\": [\n",
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       "            \"data\": [\n",
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       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u5546\\u5bb6A\": true,\n",
       "                \"\\u5546\\u5bb6B\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
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       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
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       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
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       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u5c0f\\u7c73\",\n",
       "                \"\\u4e09\\u661f\",\n",
       "                \"\\u534e\\u4e3a\",\n",
       "                \"\\u82f9\\u679c\",\n",
       "                \"\\u9b45\\u65cf\",\n",
       "                \"VIVO\",\n",
       "                \"OPPO\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"Scatter-VisualMap(Size)\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"continuous\",\n",
       "        \"min\": 20,\n",
       "        \"max\": 150,\n",
       "        \"inRange\": {\n",
       "            \"symbolSize\": [\n",
       "                20,\n",
       "                50\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 140,\n",
       "        \"borderWidth\": 0\n",
       "    }\n",
       "};\n",
       "                chart_89757ce98d60406cb46df741645c9159.setOption(option_89757ce98d60406cb46df741645c9159);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1e97bcd64f0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = (\n",
    "    Scatter()\n",
    "    .add_xaxis(df[\"名称\"].values.tolist())\n",
    "    .add_yaxis(\"商家A\", df[\"商家A\"].values.tolist())\n",
    "    .add_yaxis(\"商家B\", df[\"商家B\"].values.tolist())\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Scatter-VisualMap(Size)\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(type_=\"size\", max_=150, min_=20),\n",
    "    )\n",
    ")\n",
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcb09da",
   "metadata": {},
   "source": [
    "### 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32233ffe",
   "metadata": {},
   "source": [
    "- matplotlib和seaborn更偏向于数据可视化探索\n",
    "- pyecharts偏向于数据结果可视化展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3f08dec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c5e1749",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83739e1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc67eef3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ccee4dc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ddebde8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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